Assisting the implementation of screening for type 1 diabetes by using artificial intelligence on publicly available data

Katsarou A, Gudbjörnsdottir S, Rawshani A et al (2017) Type 1 diabetes mellitus. Nat Rev Dis Primers 3:17016. https://doi.org/10.1038/nrdp.2017.16

Article  PubMed  Google Scholar 

Rogol AD, Laffel LM, Bode B, Sperling MA (2023) Celebration of a century of insulin therapy in children with type 1 diabetes. Arch Dis Child 108(1):3–10. https://doi.org/10.1136/archdischild-2022-323975

Article  PubMed  Google Scholar 

Rawshani A, Franzén S, Eliasson B et al (2017) Mortality and cardiovascular disease in type 1 and type 2 diabetes. N Engl J Med 376(15):1407–1418. https://doi.org/10.1056/NEJMoa1608664

Article  PubMed  Google Scholar 

Persson S, Dahlquist G, Gerdtham UG, Steen Carlsson K (2018) Why childhood-onset type 1 diabetes impacts labour market outcomes: a mediation analysis. Diabetologia 61(2):342–353. https://doi.org/10.1007/s00125-017-4472-3

Article  PubMed  Google Scholar 

Rydén A, Sörstadius E, Bergenheim K et al (2016) The humanistic burden of type 1 diabetes mellitus in Europe: examining health outcomes and the role of complications. PLoS One 11(11):e0164977. https://doi.org/10.1371/journal.pone.0164977

Article  CAS  PubMed  PubMed Central  Google Scholar 

Rawshani A, Sattar N, Franzen S et al (2018) Excess mortality and cardiovascular disease in young adults with type 1 diabetes in relation to age at onset: a nationwide, register-based cohort study. Lancet 392(10146):477–486. https://doi.org/10.1016/s0140-6736(18)31506-x

Article  PubMed  PubMed Central  Google Scholar 

Warshauer JT, Bluestone JA, Anderson MS (2020) New frontiers in the treatment of type 1 diabetes. Cell Metab 31(1):46–61. https://doi.org/10.1016/j.cmet.2019.11.017

Article  CAS  PubMed  Google Scholar 

Hagopian WA, Erlich H, Lernmark A et al (2011) The Environmental Determinants of Diabetes in the Young (TEDDY): genetic criteria and international diabetes risk screening of 421 000 infants. Pediatr Diabetes 12(8):733–743. https://doi.org/10.1111/j.1399-5448.2011.00774.x

Article  PubMed  PubMed Central  Google Scholar 

Kupila A, Muona P, Simell T et al (2001) Feasibility of genetic and immunological prediction of type I diabetes in a population-based birth cohort. Diabetologia 44(3):290–297. https://doi.org/10.1007/s001250051616

Article  CAS  PubMed  Google Scholar 

Ziegler AG, Hummel M, Schenker M, Bonifacio E (1999) Autoantibody appearance and risk for development of childhood diabetes in offspring of parents with type 1 diabetes: the 2-year analysis of the German BABYDIAB Study. Diabetes 48(3):460–468. https://doi.org/10.2337/diabetes.48.3.460

Article  CAS  PubMed  Google Scholar 

Ziegler AG, Kick K, Bonifacio E et al (2020) Yield of a public health screening of children for islet autoantibodies in Bavaria, Germany. JAMA 323(4):339–351. https://doi.org/10.1001/jama.2019.21565

Article  CAS  PubMed  PubMed Central  Google Scholar 

Wion E, Brantley M, Stevens J et al (2003) Population-wide infant screening for HLA-based type 1 diabetes risk via dried blood spots from the public health infrastructure. Ann N Y Acad Sci 1005:400–403. https://doi.org/10.1196/annals.1288.067

Article  PubMed  Google Scholar 

Ludvigsson J (2021) When is screening for type 1 diabetes in children justified? J Pediatr Neonatol 2(1):17–19

Google Scholar 

Elding Larsson H, Vehik K, Bell R et al (2011) Reduced prevalence of diabetic ketoacidosis at diagnosis of type 1 diabetes in young children participating in longitudinal follow-up. Diabetes Care 34(11):2347–2352. https://doi.org/10.2337/dc11-1026

Article  PubMed  PubMed Central  Google Scholar 

Smith LB, Liu X, Johnson SB et al (2018) Family adjustment to diabetes diagnosis in children: can participation in a study on type 1 diabetes genetic risk be helpful? Pediatr Diabetes 19(5):1025–1033. https://doi.org/10.1111/pedi.12674

Article  PubMed  PubMed Central  Google Scholar 

Hekkala AM, Ilonen J, Toppari J, Knip M, Veijola R (2018) Ketoacidosis at diagnosis of type 1 diabetes: effect of prospective studies with newborn genetic screening and follow up of risk children. Pediatr Diabetes 19(2):314–319. https://doi.org/10.1111/pedi.12541

Article  CAS  PubMed  Google Scholar 

den Hollander NHM, Roep BO (2022) From disease and patient heterogeneity to precision medicine in type 1 diabetes. Front Med (Lausanne) 9:932086. https://doi.org/10.3389/fmed.2022.932086

Article  Google Scholar 

Melin J, Maziarz M, AndrénAronsson C, Lundgren M, Elding Larsson H (2020) Parental anxiety after 5 years of participation in a longitudinal study of children at high risk of type 1 diabetes. Pediatr Diabetes 21(5):878–889. https://doi.org/10.1111/pedi.13024

Article  CAS  PubMed  Google Scholar 

Hallak R (2023) AI's biggest promise: the democratization of precision medicine. Available from: https://www.forbes.com/sites/forbestechcouncil/2023/01/24/ais-biggest-promise-the-democratization-of-precision-medicine/?sh=4e6cd1521ba1. Accessed 20 Sep 2023

Taylor CR, Monga N, Johnson C, Hawley JR, Patel M (2023) Artificial intelligence applications in breast imaging: current status and future directions. Diagnostics (Basel) 13(12):2041. https://doi.org/10.3390/diagnostics13122041

Article  CAS  PubMed  Google Scholar 

Huang S, Yang J, Fong S, Zhao Q (2020) Artificial intelligence in cancer diagnosis and prognosis: opportunities and challenges. Cancer Lett 471:61–71. https://doi.org/10.1016/j.canlet.2019.12.007

Article  CAS  PubMed  Google Scholar 

Chiarito M, Luceri L, Oliva A, Stefanini G, Condorelli G (2022) Artificial intelligence and cardiovascular risk prediction: all that glitters is not gold. Eur Cardiol 17:e29. https://doi.org/10.15420/ecr.2022.11

Article  PubMed  PubMed Central  Google Scholar 

Choi MY, Chen I, Clarke AE et al (2023) Machine learning identifies clusters of longitudinal autoantibody profiles predictive of systemic lupus erythematosus disease outcomes. Ann Rheum Dis 82(7):927–936. https://doi.org/10.1136/ard-2022-223808

Article  CAS  PubMed  Google Scholar 

Forrest IS, Petrazzini BO, Duffy Á et al (2023) A machine learning model identifies patients in need of autoimmune disease testing using electronic health records. Nat Commun 14(1):2385. https://doi.org/10.1038/s41467-023-37996-7

Article  CAS  PubMed  PubMed Central  Google Scholar 

Shapiro J, Getz B, Cohen SB et al (2023) Evaluation of a machine learning tool for the early identification of patients with undiagnosed psoriatic arthritis - a retrospective population-based study. J Transl Autoimmun 7:100207. https://doi.org/10.1016/j.jtauto.2023.100207

Article  CAS  PubMed  PubMed Central  Google Scholar 

Nimri R, Battelino T, Laffel LM et al (2020) Insulin dose optimization using an automated artificial intelligence-based decision support system in youths with type 1 diabetes. Nat Med 26(9):1380–1384. https://doi.org/10.1038/s41591-020-1045-7

Article  CAS  PubMed  Google Scholar 

Nakayasu ES, Bramer LM, Ansong C et al (2023) Plasma protein biomarkers predict the development of persistent autoantibodies and type 1 diabetes 6 months prior to the onset of autoimmunity. Cell Rep Med 4(7):101093. https://doi.org/10.1016/j.xcrm.2023.101093

Article  CAS  PubMed  PubMed Central  Google Scholar 

Ipp E, Liljenquist D, Bode B et al (2021) Pivotal evaluation of an artificial intelligence system for autonomous detection of referrable and vision-threatening diabetic retinopathy. JAMA Netw Open 4(11):e2134254. https://doi.org/10.1001/jamanetworkopen.2021.34254

Article  PubMed  PubMed Central  Google Scholar 

Krischer JP, Liu X, Vehik K et al (2019) Predicting islet cell autoimmunity and type 1 diabetes: an 8-year TEDDY study progress report. Diabetes Care 42(6):1051–1060. https://doi.org/10.2337/dc18-2282

Article  CAS  PubMed  PubMed Central  Google Scholar 

Beyerlein A, Bonifacio E, Vehik K et al (2019) Progression from islet autoimmunity to clinical type 1 diabetes is influenced by genetic factors: results from the prospective TEDDY study. J Med Genet 56(9):602–605. https://doi.org/10.1136/jmedgenet-2018-105532

Article  CAS  PubMed  Google Scholar 

Sharp SA, Rich SS, Wood AR et al (2019) Development and standardization of an improved type 1 diabetes genetic risk score for use in newborn screening and incident diagnosis. Diabetes Care 42(2):200–207. https://doi.org/10.2337/dc18-1785

Article  CAS  PubMed  PubMed Central  Google Scholar 

European Medicines Agency (2022) Qualification opinion of islet autoantibodies (AAs) as enrichment biomarkers for type 1 diabetes (T1D) prevention clinical trials. Available from: https://www.ema.europa.eu/en/documents/regulatory-procedural-guideline/qualification-opinion-islet-autoantibodies-aas-enrichment-biomarkers-type-1-diabetes-t1d-prevention-clinical-trials_en.pdf. Accessed 20 Sep 2023

Carr ALJ, Evans-Molina C, Oram RA (2022) Precision medicine in type 1 diabetes. Diabetologia 65(11):1854–1866. https://doi.org/10.1007/s00125-022-05778-3

Article  PubMed  PubMed Central  Google Scholar 

Sgaier S, Dominici F (2019) Using AI to understand what causes diseases. Harvard Business Review. Available from: https://hbr.org/2019/11/using-ai-to-understand-what-causes-diseases. Accessed 20 Sep 2023

Placido D, Yuan B, Hjaltelin JX et al (2023) A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories. Nat Med 29(5):1113–1122. https://doi.org/10.1038/s41591-023-02332-5

Article  CAS  PubMed 

留言 (0)

沒有登入
gif